Many quantitative disciplines collect or generate multidimensional data. These disciplines include medical imaging applications such as CT or MRI; geophysical modeling, meteorological forecasting, scientific simulations, animation models, and the like. This multidimensional data is often stored and manipulated in the form of voxels. Voxels are volume elements in three (or more) dimensions; and are analogous to pixels (two dimensional picture elements).
Professionals often find it useful to be able to visualize some aspect of voxel data. The visualization requires transforming the voxel data, so that a cross-section, a projection, or another form of visualization can be realized on a two-dimensional display device. Numerous visualization techniques have been explored, and most are unfeasible for application; by reason of computational complexities associated therewith. Nevertheless, there are certain basic desirable aspects of visualization standards for accepted renderings (representations).
The professional expects the visualization to facilitate elevated insights and to evoke increased understanding of the data. This is often accomplished by imposing (onto a rendering of the data) subjective criteria such as depth, shading, perspective, lighting, or shadowing; which are not necessarily generic to the data being rendered. For example, depth or shadow are not natural features of geophysical cross-sections; but may be helpful to the professional who is looking for ways to understand such a complex data set. Alternately, the professional may expect the visualization to be life-like (of realistic appearance).
The result of the professionals' rendering expectations and the computational complexity of accomplishing them has generated a cluttered convolution of rendering techniques. Some techniques have been developed which are specific to rendering certain data sets, while other techniques are seemingly more general in scope.
The nature of the prior art (of rendering a voxel space) can be better appreciated from studying U.S. Pat. No. 5,201,035, U.S. Pat. No. 5,499,323, U.S. Pat. No. 5,594,844, and from the prior art references cited therein. Furthermore, the order of complexity required for successful algorithmic optimization, in forming a perspective rendering from a voxel space, will thereby be appreciated.
The prior art is problematic and primarily application specific. Many overlapping combinations of more fundamental graphics algorithms are used in an attempt to simultaneously provide adequate rendering within algorithmic bounds that are economically and technically practical. Many examples of prior art methods are visually realistic but algorithmically heavy, and many other examples of the prior art are visually simplistic albeit algorithmically feasible. Thus, there is a need in the art for rendering methods that are simultaneously visually realistic and algorithmically practical.